mlsanityData to 7 Oct 2026

Accelerating Diffusion Models for Generative AI Applications with Silicon Photonics

arXiv 2603.07626 ↗First posted 8 Mar 2026cs.AR cs.LGcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Diffusion models have revolutionized generative AI, with their inherent capacity to generate highly realistic state-of-the-art synthetic data. However, these models employ an iterative denoising process over computationally intensive layers such as UNets and attention mechanisms. This results in high inference energy on conventional electronic platforms, and thus, there is an emerging need to accelerate these models in a sustainable manner. To address this challenge, we present a novel silicon photonics-based accelerator for diffusion models. Experimental evaluations demonstrate that our photonic accelerator achieves at least 3x better energy efficiency and 5.5x throughput improvement compared to state-of-the-art diffusion model accelerators.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 18 of its 34 references that are in the map. First posted on arXiv 8 Mar 2026.

Builds on

18 of 34 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023DeepCache: Accelerating Diffusion Models for Free1 Dec
  2. 2023Speed Is All You Need: On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizations21 Apr
  3. 2023Q-Diffusion: Quantizing Diffusion Models8 Feb
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  5. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 13

  1. 2018A Note on the Inception Scorecited by 225
  2. 2021A Survey on Silicon Photonics for Deep Learningcited by 23
  3. 2021ROBIN: A Robust Optical Binary Neural Network Acceleratorcited by 15
  4. 2023Lightening-Transformer: A Dynamically-operated Optically-interconnected Photonic Transformer Acceleratorcited by 15
  5. 2022RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonicscited by 12
  6. 2023TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonicscited by 12
  7. 2021SONIC: A Sparse Neural Network Inference Accelerator with Silicon Photonics for Energy-Efficient Deep Learningcited by 10
  8. 2022A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantizationcited by 9
  9. 2018PCNNA: A Photonic Convolutional Neural Network Acceleratorcited by 8
  10. 2023GHOST: A Graph Neural Network Accelerator using Silicon Photonicscited by 7
  11. 2024SafeLight: Enhancing Security in Optical Convolutional Neural Network Acceleratorscited by 4
  12. 2024OPIMA: Optical Processing-In-Memory for Convolutional Neural Network Accelerationcited by 1
1 more
  1. 2025PhotoGAN: Generative Adversarial Neural Network Acceleration with Silicon Photonicscited by 1

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  1. 2024Optical Diffusion Models for Image Generation
  2. 2025SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity
  3. 2023Quantum circuit synthesis with diffusion models
  4. 2023Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation
  5. 2024Quantum Denoising Diffusion Models
  6. 2025Physics-inspired Generative AI models via real hardware-based noisy quantum diffusion
  7. 2025CHORDS: Diffusion Sampling Accelerator with Multi-core Hierarchical ODE Solvers
  8. 2025Physics-guided and fabrication-aware inverse design of photonic devices using diffusion models